1. Technical Field
This disclosure relates to networked systems. More particularly, the present disclosure relates to creating topic neighborhoods and a visualization for related topic neighborhoods in a networked system.
2. Related Art
Existing search engines generally use keyword-based categorization, indexing, matching, presenting, navigating, ranking, and managing a plurality of documents. Ranking algorithms generally establish a simple ordering of the documents retrieved in response to a query. Documents appearing at the top of this ordering are considered to be more likely to be relevant. Different global and local ranking schemes have been used to establish documents such as web pages that are relevant to the likelihood of user's needs. These methods include variations of the page rank algorithm, which establishes a global ranking based on links to and from web pages, simulates a random browsing of internet and estimates the likelihood that a user, by random navigation, can arrive at a particular page. While such methods can provide a global score of potential user viewing behavior, it does not take into account the cognitive aspect of content and knowledge associated with web pages. Some approaches have been used to attribute local importance to pages. However, these approaches require an initial query result against which the relevance of the related pages in World Wide Web or document databases can be measured. One of the primary drawbacks of this method is that it has to be carried out in real time; i.e. after a query has been submitted made and a set of results obtained, the algorithm attempts to crawl the neighborhood of these results in real-time to find related pages. Moreover these methods do not detect cases where a node has exerted “undue influence” on the computation of scores, and documents in a community, i.e., the relevant documents, are not ranked.
The most popular method of context creation is through manual grouping of relevant content that can be manifested in directories or any other manual link and content listing. However, these listings can quickly become obsolete due to the dynamic nature of the World Wide Web and the scale of data can also render manual editing of data impracticable or inefficient.
Current search engine and information retrieval systems allow users to personalize and share their keyword searches within their online social/professional networks, and also enable users to add tags and additional information on search results. Often these results are summarized as single URLs or via access to a manually generated list of relevant links. This approach has many fundamental problems, namely knowledge is mostly a collection of related documents and contents, and not single documents and contents. Moreover, the same content can appear in multiple knowledge bases with different relevancies, and a knowledge base may comprise a dynamic set of documents, i.e., a document over time may lose its importance to a knowledge context and new documents may become more relevant. Thus, static lists of documents provide an inefficient means for sharing information. People would prefer to share knowledge and not keywords or single documents.
United States Patent Application No. 20070198506 discloses methods and systems for creating, managing, searching, personalizing, and monetizing a knowledge system defined over a corpus of digital content. Systems and methods are described in which a user can initiate in-depth searches of subject matter and can browse, navigate, pinpoint, and select relevant contexts, concepts, and documents to gain knowledge. Systems and methods are described in which knowledge can be personalized through tagged, personalized context, and personalized context can be shared within social and professional networks, securely and confidentially and with the desired access control. Systems and methods are described in which products and services can be advertised in context and advertising can be selected through a bidding process. Systems and methods are described by which a user can navigate contexts and concepts to obtain relevant information, products and services.
However, current systems do not provide a means for aggregating a collection of knowledge bases and communication channels that can be shared among a set of users who share an interest in a particular topic.
Thus, a computer-implemented system and method for creating topic neighborhoods and a visualization for related topic neighborhoods in a networked system is needed.